Data masking for recommender systems

Abstract

Data science challenges allow companies, and other data holders, to collaborate with the wider research community. In the area of recommender systems, the potential of such challenges to move forward the state of the art is limited due to concerns about releasing user interaction data. This paper investigates the potential of privacy-preserving data publishing for supporting recommender system challenges. We propose a data masking algorithm, Shuffle-NNN, with two steps: Neighborhood selection and value swapping. Neighborhood selection preserves valuable item similarity information. The data shuffling technique hides (i.e., changes) ratings of users for individual items. Our experimental results demonstrate that the relative performance of algorithms, which is the key property that a data science challenge must measure, is comparable between the original data and the data masked with Shuffle-NNN.